model.py

#! usr/bin/env python3

__name__ ='Akash Manna'
__date__ ='02/01/2022'

# Import modules and packages
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

#Functions and procedures
def plot_predictions(train_data, train_labels, test_data, test_labels, predictions):
    '''
    plots training data, test data and compare predictions
    '''
    plt.figure(figsize= (6, 5))

    #plot training data in blue
    plt.scatter(train_data, train_labels, c= 'b', label= 'Training data')

    #Plot test data in green
    plt.scatter(test_data, test_labels, c= 'g', label= 'Testing data')

    #Plot the prediction in red
    plt.scatter(test_data, predictions, c= 'r', label= 'predictions')

    #Show the legends
    plt.legend(shadow= 'True')

    #Set grids
    plt.grid(which= 'major', c= '#cccccc', linestyle= '--', alpha= 0.5)

    #Some text
    plt.title('Model Results', family= 'Arial', fontsize= 14)
    plt.xlabel('X axis values', family= 'Arial', fontsize=11)
    plt.ylabel('Y axis values', family = 'Arial', fontsize= 11)

    #Show
    plt.savefig('model_results.png', dpi= 120)


def mae(y_test, y_pred):
    '''
    Calculates the MAE between y_test and y_preds
    '''
    return tf.metrics.mean_absolute_error(y_test, y_pred)


def mse(y_test, y_pred):
    '''
    Calculates the mean squared error between y_test and y_pred
    '''
    return tf.metrics.mean_squared_error(y_test, y_pred)

#Check the tensorflow version
print(tf.__version__)

#Create features
X = np.arange(-100, 100, 4)

#Create labels
y = np.arange(-90, 110, 4)

#Split data info train and test sets
X_train = X[:40]
y_train = y[:40]

X_test= X[40:]
y_test = y[40:]

# Take a single example of X
input_shape = X[0].shape

#take a single example of y
output_shape = y[0].shape


#Set random seed
tf.random.set_seed(42)


#Craete the model
model = tf.keras.Sequential(
    [
        tf.keras.layers.Dense(1, input_shape=[1]),
        tf.keras.layers.Dense(1),
        tf.keras.layers.Dense(1)
    ]
)


#compile the model
model.compile(
    loss = tf.keras.losses.mae,
    optimizer = tf.keras.optimizers.SGD(),
    metrics = ['mae']
)


#Fit the model
model.fit(X_train, y_train, epochs =1000)


#Make plot and predictions of the model
y_preds = model.predict(X_test)
plot_predictions(train_data= X_train, train_labels= y_train,
                test_data= X_test, test_labels= y_test, predictions= y_preds)

#Calculate the model metrics
mae_1 = np.round(float(mae(y_test, y_preds.squeeze()).numpy()), 2)
mse_1 = np.round(float(mse(y_test, y_preds.squeeze()).numpy()), 2)
print(f'\n Mean Absolute error = {mae_1}, mean Squred Error = {mse_1}.')

# #Write metrics to file
# with open('metrics.txt', 'w') as outfile:
#     outfile.write(f'\n Mean Absolute Error = {mae_1}, Mean Squred error = {mse_1}. ')